Bridging maladaptive social self-beliefs and social anxiety: a network perspective

Abstract

The Clark and Wells (1995) model of social anxiety disorder postulates that three types of maladaptive social self-beliefs (high standard, conditional, and unconditional beliefs) play a crucial role in the development of fear and avoidance of social-evaluative situations-i.e., the hallmark symptoms of social anxiety disorder. In this project, we examined associations between the three types of maladaptive social self-beliefs and fear and avoidance of socialevaluative situations in a nonclinical community sample (n = 389). We used network analysis to estimate functional relations among aspects of maladaptive self-beliefs, fear, and avoidance and computed two different network models, a graphical Gaussian model (GGM) and a directed acyclic graph (DAG). Each model estimates edges and the importance of nodes in different ways. Both GGM and DAG pointed to fear and conditional beliefs as especially potent bridges between maladaptive social self-beliefs and social anxiety in our nonclinical sample. Altogether, these results offer data-driven heuristics in the field's larger, ongoing effort to illuminate pathways at play in the development of social anxiety. We situate this study within novel network approaches for developing theory-driven models and tests of the instigation and interactions of maladaptive social self-beliefs and social anxiety. However, because this is the first study to combine GGM and DAG in social anxiety research, we also discussed the caveats to this approach to help to usher the field forward.

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A Network Approach to Parental Burnout

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Excessive Worrying as a Central Feature of Anxiety during the First COVID-19 Lockdown-Phase in Belgium: Insights from a Network Approach

“…Finally, we followed recent publications (e.g., Bernstein et al, 2017;Blanchard et al, 2021;Heeren et al, 2020;McNally et al, 2017, Moffa et al, 2017 and relied on Bayesian network methods to estimate a directed acyclic graph (DAG), which encodes the conditional independence relationships between the variables of interest and characterizes their joint probability distribution (McNally, 2016;Pearl et al, 2016;Scutari, 2010). Hence, the resulting network is directed and possesses arrows reflecting the predicted direction of the probabilistic dependence between nodes (McNally, 2016;Moffa et al, 2017).…”